SWWA Connectivity: Untitled

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library(GeoPressureR)
library(tidyverse)
library(leaflet)
library(leaflet.extras)
library(raster)
library(dplyr)
library(ggplot2)
library(plotly)
knitr::opts_chunk$set(echo = FALSE)
load(paste0("../data/1_pressure//", params$gdl_id, "_pressure_prob.Rdata"))
load(paste0("../data/3_static/", params$gdl_id, "_static_prob.Rdata"))
# load(paste0("../data/4_basic_graph/", params$gdl_id, "_basic_graph.Rdata"))
load(paste0("../data/5_wind_graph/", params$gdl_id, "_wind_graph.Rdata"))
load(paste0("../data/5_wind_graph/", params$gdl_id, "_grl.Rdata"))
col <- rep(RColorBrewer::brewer.pal(8, "Dark2"), times = ceiling(max(pam$sta$sta_id) / 8))

Altitude

Altitudes are computed based on pressure measurement of the geolocation, corrected based on the assumed location of the shortest path. This correction accounts therefore for the natural variation of pressure as estimated by ERA-5. The vertical lines indicate the sunrise (dashed) and sunset (solid).

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p <- ggplot() +
  # geom_line(data = pam$pressure, aes(x = date, y = obs), colour = "grey") +
  geom_line(data = do.call("rbind", shortest_path_timeserie), aes(x = date, y = altitude)) +
  geom_line(data = do.call("rbind", shortest_path_timeserie) %>% filter(sta_id > 0), aes(x = date, y = altitude, col = factor(sta_id))) +
  # geom_vline(data = twl, aes(xintercept = twilight, linetype = ifelse(rise, "dashed", "solid"), color="grey"), lwd=0.1) +
  theme_bw() +
  scale_colour_manual(values = col) +
  scale_y_continuous(name = "Altitude (m)")

ggplotly(p, dynamicTicks = T) %>% layout(showlegend = F)

Wintering location

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file <- paste0("figure_print/wintering_location/wintering_location_",params$gdl_id,".png")
if(file.exists(file)){
  knitr::include_graphics(file)
}

Latitude time

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 tmp <- lapply(pressure_prob, function(x) {
    mt <- metadata(x)
    df <- data.frame(
      start = mt$temporal_extent[1],
      end = mt$temporal_extent[2],
      sta_id = mt$sta_id
    )
  })
  tmp2 <- do.call("rbind", tmp)

sim_lat <- as.data.frame(t(path_sim$lat)) %>%
  mutate(sta_id = path_sim$sta_id) %>%
  pivot_longer(-c(sta_id)) %>%
  left_join(tmp2,by="sta_id")

sim_lat_p <- sim_lat %>%
  filter(sta_id==max(sta_id)) %>%
  mutate(start=end) %>%
  rbind(sim_lat)

sp_lat <- as.data.frame(shortest_path) %>% left_join(tmp2,by="sta_id")

sp_lat_p <- sp_lat %>%
  filter(sta_id==max(sta_id)) %>%
  mutate(start=end) %>%
  rbind(sp_lat)

p <- ggplot() +
  geom_step(data=sim_lat_p, aes(x=start, y=value, group=name), alpha=.07) +
  geom_point(data=sp_lat_p, aes(x=start, y=lat)) +
  xlab('Date') +
  ylab('Latitude') +
  theme_light()

ggplotly(p, dynamicTicks = T)

Shortest path and simulated path

The large circles indicates the shortest path (overall most likely trajectory) estimated by the graph approach. The size is proportional to the duration of stay. The small dots and grey lines represents 10 possible trajeectories of the bird according to the model.

Click on the full-screen mode button on the top-left of the map to see more details on the map.

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sta_duration <- unlist(lapply(static_prob_marginal, function(x) {
  as.numeric(difftime(metadata(x)$temporal_extent[2], metadata(x)$temporal_extent[1], units = "days"))
}))
pal <- colorFactor(col, as.factor(seq_len(length(col))))
m <- leaflet(width = "100%") %>%
  addProviderTiles(providers$Stamen.TerrainBackground) %>%
  addFullscreenControl() %>%
  addPolylines(lng = shortest_path$lon, lat = shortest_path$lat, opacity = 1, color = "#808080", weight = 3) %>%
  addCircles(lng = shortest_path$lon, lat = shortest_path$lat, opacity = 1, color = pal(factor(shortest_path$sta_id, levels = pam$sta$sta_id)), weight = sta_duration^(0.3) * 10)

for (i in seq_len(nrow(path_sim$lon))) {
  m <- m %>%
    addPolylines(lng = path_sim$lon[i, ], lat = path_sim$lat[i, ], opacity = 0.5, weight = 1, color = "#808080") %>%
    addCircles(lng = path_sim$lon[i, ], lat = path_sim$lat[i, ], opacity = .7, weight = 1, color = pal(factor(shortest_path$sta_id, levels = pam$sta$sta_id)))
}
m

Marginal probability map

The marginal probability map estimate the overall probability of position at each stationary period regardless of the trajectory taken by the bird. It is the most useful quantification of the uncertainty of the position of the bird.

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li_s <- list()
l <- leaflet(width = "100%") %>%
  addProviderTiles(providers$Stamen.TerrainBackground) %>%
  addFullscreenControl()
for (i_r in seq_len(length(static_prob_marginal))) {
  i_s <- metadata(static_prob_marginal[[i_r]])$sta_id
  info <- metadata(static_prob_marginal[[i_r]])$temporal_extent
  info_str <- paste0(i_s, " | ", info[1], "->", info[2])
  li_s <- append(li_s, info_str)
  l <- l %>%
    addRasterImage(static_prob_marginal[[i_r]], colors = "OrRd", opacity = 0.8, group = info_str) %>%
    addCircles(lng = shortest_path$lon[i_s], lat = shortest_path$lat[i_s], opacity = 1, color = "#000", weight = 10, group = info_str)
}
l %>%
  addLayersControl(
    overlayGroups = li_s,
    options = layersControlOptions(collapsed = FALSE)
  ) %>%
  hideGroup(tail(li_s, length(li_s) - 1))

Wind assistance

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  fun_marker_color <- function(norm){
    if (norm < 20){
      "darkpurple"
    } else if (norm < 35){
      "darkblue"
    } else if (norm < 50){
      "lightblue"
    } else if (norm < 60){
      "lightgreen"
    } else if (norm < 80){
      "yellow"
    } else if (norm < 100){
      "lightred"
    } else {
      "darkred"
    }
  }
  fun_NSEW <- function(angle){
    angle <- angle  %% (pi* 2)
    angle <- angle*180/pi
    if (angle < 45/2){
      "E"
    } else if (angle < 45*3/2){
      "NE"
    } else if (angle < 45*5/2){
      "N"
    } else if (angle < 45*7/2){
      "NW"
    } else if (angle < 45*9/2){
      "W"
    } else if (angle < 45*11/2){
      "SW"
    } else if (angle < 45*13/2){
      "S"
    }else if (angle < 45*15/2){
      "SE"
    } else {
      "E"
    }
  }

  sta_duration <- unlist(lapply(static_prob_marginal,function(x){as.numeric(difftime(metadata(x)$temporal_extent[2],metadata(x)$temporal_extent[1],units="days"))}))

  m <-leaflet(width = "100%") %>%
    addProviderTiles(providers$Stamen.TerrainBackground) %>%  addFullscreenControl() %>%
    addPolylines(lng = shortest_path$lon, lat = shortest_path$lat, opacity = 1, color = "#808080", weight = 3) %>%
    addCircles(lng = shortest_path$lon, lat = shortest_path$lat, opacity = 1, color = "#000", weight = sta_duration^(0.3)*10)

  for (i_s in seq_len(grl$sz[3]-1)){
    if (grl$flight_duration[i_s]>5){
      edge <- which(grl$s == shortest_path$id[i_s] & grl$t == shortest_path$id[i_s+1])

      label = paste0( i_s,': ', grl$flight[[i_s]]$start, " - ", grl$flight[[i_s]]$end, "<br>",
                      "F. dur.: ", round(grl$flight_duration[i_s]), ' h <br>',
                      "GS: ", round(abs(grl$gs[edge])), ' km/h, ',fun_NSEW(Arg(grl$gs[edge])),'<br>',
                      "WS: ", round(abs(grl$ws[edge])), ' km/h, ',fun_NSEW(Arg(grl$ws[edge])),'<br>',
                      "AS: ", round(abs(grl$as[edge])), ' km/h, ',fun_NSEW(Arg(grl$as[edge])),'<br>')

      iconArrow <- makeAwesomeIcon(icon = "arrow-up",
                                   library = "fa",
                                   iconColor = "#FFF",
                                   iconRotate = (90 - Arg(grl$ws[edge])/pi*180) %% 360,
                                   squareMarker = TRUE,
                                   markerColor = fun_marker_color(abs(grl$ws[edge])))

      m <- m %>% addAwesomeMarkers(lng = (shortest_path$lon[i_s] + shortest_path$lon[i_s+1])/2,
                                   lat = (shortest_path$lat[i_s] + shortest_path$lat[i_s+1])/2,
                                   icon = iconArrow, popup = label)
    }
  }
  m

Histogram of Speed

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edge <- t(graph_path2edge(path_sim$id, grl))
nj <- ncol(edge)
nsta <- ncol(path_sim$lon)

speed_df <- data.frame(
  as = abs(grl$as[edge]),
  gs = abs(grl$gs[edge]),
  ws = abs(grl$ws[edge]),
  sta_id_s = rep(head(grl$sta_id,-1), nj),
  sta_id_t = rep(tail(grl$sta_id,-1), nj),
  flight_duration = rep(head(grl$flight_duration,-1), nj),
  dist = geosphere::distGeo(
    cbind(as.vector(t(path_sim$lon[,1:nsta-1])), as.vector(t(path_sim$lat[,1:nsta-1]))),
    cbind(as.vector(t(path_sim$lon[,2:nsta])),   as.vector(t(path_sim$lat[,2:nsta])))
  ) / 1000
) %>% mutate(
  name = paste(sta_id_s,sta_id_t, sep="-")
)

plot1 <- ggplot(speed_df, aes(reorder(name, sta_id_s), gs)) + geom_boxplot() + theme_bw() +scale_x_discrete(name = "")
plot2 <- ggplot(speed_df, aes(reorder(name, sta_id_s), ws)) + geom_boxplot() + theme_bw() +scale_x_discrete(name = "")
plot3 <- ggplot(speed_df, aes(reorder(name, sta_id_s), as)) + geom_boxplot() + theme_bw() +scale_x_discrete(name = "")
plot4 <- ggplot(speed_df, aes(reorder(name, sta_id_s), flight_duration)) + geom_point() + theme_bw() +scale_x_discrete(name = "")

subplot(ggplotly(plot1), ggplotly(plot2), ggplotly(plot3), ggplotly(plot4), nrows=4, titleY=T)

Table of transition

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alt_df = do.call("rbind", shortest_path_timeserie) %>%
    arrange(date) %>%
    mutate(
      sta_id_s = cummax(sta_id),
      sta_id_t = sta_id_s+1
    ) %>%
    filter(sta_id == 0 & sta_id_s > 0 ) %>%
    group_by(sta_id_s, sta_id_t) %>%
    summarise(
      alt_min = min(altitude),
      alt_max = max(altitude),
      alt_mean = mean(altitude),
      alt_med = median(altitude),
    )

  trans_df <- speed_df  %>%
    group_by(sta_id_s,sta_id_t,flight_duration) %>%
    summarise(
      as_m = mean(as),
      as_s = sd(as),
      gs_m = mean(gs),
      gs_s = sd(gs),
      ws_m = mean(ws),
      ws_s = sd(ws),
      dist_m = mean(dist),
      dist_s = sd(dist)
    ) %>%
    left_join(alt_df)

trans_df %>% kable()

sta_id_s	sta_id_t	flight_duration	as_m	as_s	gs_m	gs_s	ws_m	ws_s	dist_m	dist_s	alt_min	alt_max	alt_mean	alt_med
1	2	11.0	35.33608	11.106192	43.70459	15.079578	15.202896	2.2886900	480.75049	165.87536	137.98217	937.6921	487.10749	491.98736
2	4	24.0	58.08405	7.840195	60.75623	7.630828	6.051203	3.3639459	1458.14957	183.13988	NA	NA	NA	NA
4	5	4.5	30.21904	14.143127	31.55826	16.231589	9.963012	2.7849359	142.01216	73.04215	57.24094	119.7909	86.56618	86.74421
5	6	3.5	32.91453	16.824140	30.65304	15.944756	10.648041	2.0137637	107.28563	55.80665	57.82943	135.7764	79.88948	75.43465
6	7	1.0	29.98802	15.868136	33.35164	21.486298	20.541424	3.6258648	33.35164	21.48630	88.65856	166.0373	139.81633	164.75315
7	8	2.0	31.26612	15.779568	47.53278	20.459297	36.177563	2.4092188	95.06556	40.91859	180.33671	387.0460	274.87964	243.14331
8	10	22.5	43.31449	3.407421	81.01412	2.948151	37.902387	0.9631061	1822.81760	66.33339	NA	NA	NA	NA
10	11	3.0	46.36092	10.372802	30.66572	16.013707	28.115650	2.8038219	91.99715	48.04112	114.32751	179.4176	141.61231	144.39294
11	12	1.5	51.86457	12.779206	42.92170	14.780803	18.400147	0.8338496	64.38255	22.17121	197.13893	284.1933	247.10065	253.53520

CB608

Altitude

Wintering location

Latitude time

Shortest path and simulated path

Marginal probability map

Wind assistance

Histogram of Speed

Table of transition